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A Collaborative Optimization Algorithm for RBF Neural Network Based on Activity Density
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DOI：10.1007/s11063-026-11877-8.png)
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Abstract
En 中文
The RBF neural network (RBFNN) is proposed to address the challenges of modeling nonlinear dynamic systems. However, the modeling effectiveness is often affected by the insufficient adjustment of the structure and parameters. To address this problem, a collaborative optimization algorithm based on activity density (AD-RBFNN) is proposed in this paper for training the RBFNN. First, an activity density (AD) is introduced to characterize the contribution of neurons, which represents the average ratio of the output to the width of the neuron across the entire sample space. Second, the parameters and structure of the RBFNN are tuned based on the activity density to achieve an effective tuning result collaboratively. Third, a compensation mechanism is designed to eliminate the errors caused by the adjustment of the structure. Finally, the analysis of error-boundedness theory is provided to guarantee the stability of this method, ensuring the successful deployment of the AD-RBFNN. To validate the effectiveness of the designed AD-RBFNN, it is applied to nonlinear function approximation and industrial wastewater treatment process modeling, and compared against other mainstream algorithms. Results demonstrate that the AD-RBFNN exhibits significant advantages in both model accuracy and generalization capability, offering an effective intelligent modeling for complex industrial processes.
Keywords:
Activity density
Collaborative optimization
Nonlinear system modeling
RBF neural network
Journal
IF:
2.8
Papers:
169
Citations:
5.5K